Accelerating the projection of heatwaves, droughts, and compound events through high-fidelity ML emulation of climate models.

Accelerating the projection of heatwaves, droughts, and compound events through high-fidelity ML emulation of climate models.

Plant functional traits govern ecosystem processes but lack spatially detailed global representations. Here, the authors combine remote sensing and crowd-sourced biodiversity data to map leaf trait di...

Artificial intelligence for modeling and understanding extreme weather and climate events

Read the full article here: https://tr.ee/FlagDecompositionFlag manifolds encode hierarchical nested sequences of subspaces and serve as powerful structures ...

Read the full paper here: https://tr.ee/InvertibleNeuralNetworksSatellite remote sensing is the primary source of global aerosol observations, providing esse...

Viewed from space, a “green wave” seasonally traverses Earth’s surface, from the north in boreal summer to the south in austral summer. This wave r...

Contribute to kipoju/udl4fl development by creating an account on GitHub.

"Understanding flood detection models across Sentinel-1 and Sentinel-2 modalities and benchmark datasets"Read the full article here: https://tr.ee/Understand...

HighlightsWhat are the main findings?We have developed a machine-learning algorithm able to quantify high-turbid environments.We use open-source and free databases to train the model and open-source and free tools to develop it, which makes it easily replicable and transferable.What are the implications of the main findings?The social impact of our work implies improved monitoring of areas with high turbidity, which can lead to a better understanding and use of forecasting.Ocean color and water quality communities can take advantage of the lessons learned for developing new products or services.

Learn about dimensionality reduction, radiative transfer models, explainable AI, hyperspectral imaging, spatial information, and more.isp.uv.es/courses

Read the full paper here: http://tr.ee/Sub-SeasonalForestCarbonDynamicsExtreme weather events pose a growing threat to the stability of terrestrial ecosystem...

"Out-of-distribution robustness for multivariate analysis via causal regularisation"Read the full article here: http://tr.ee/Out-of-DistributionRobustness

Abstract. Rising summer temperatures and more frequent heat extremes are well-documented outcomes of anthropogenic climate change. However, the extent to which atmospheric circulation changes contribu...

Read the full article here: https://tr.ee/GapFillingSentinel2Earth observation from satellite sensors offers the possibility to monitor natural ecosystems by...

Here's a short recap of what has happened at the ISP during the second half of this year!#research #machinelearning #sentinel2 #earthobservation #geoscience ...

HighlightsWhat are the main findings?We have developed a machine-learning algorithm able to quantify high-turbid environments.We use open-source and free databases to train the model and open-source and free tools to develop it, which makes it easily replicable and transferable.What are the implications of the main findings?The social impact of our work implies improved monitoring of areas with high turbidity, which can lead to a better understanding and use of forecasting.Ocean color and water quality communities can take advantage of the lessons learned for developing new products or services.

HighlightsWhat are the main findings?We have developed a machine-learning algorithm able to quantify high-turbid environments.We use open-source and free databases to train the model and open-source and free tools to develop it, which makes it easily replicable and transferable.What are the implications of the main findings?The social impact of our work implies improved monitoring of areas with high turbidity, which can lead to a better understanding and use of forecasting.Ocean color and water quality communities can take advantage of the lessons learned for developing new products or services.

The experiments allowed by current machine learning models imply a revival of the debate on the causes of specific trends of human visual psychophysics. Mach...